In this course, you will explore a variety of open-source technologies for working with geosptial data, performing spatial analysis, and undertaking general data science. The first component of the class focuses on the use of QGIS and associated technologies (GDAL, PROJ, GRASS, SAGA, and Orfeo Toolbox). The second component of the class introduces Python and associated open-source libraries and modules (NumPy, Pandas, Matplotlib, Seaborn, GeoPandas, Rasterio, WhiteboxTools, and Scikit-Learn) used by geospatial scientists and data scientists. We also provide an introduction to Structured Query Language (SQL) for performing table and spatial queries. This course is designed for individuals that have a background in GIS, such as working in the ArcGIS environment, but no prior experience using open-source software and/or coding. You will be asked to work through a series of lecture modules and videos broken into several topic areas, as outlined below. Fourteen assignments and the required data have been provided as hands-on opportunites to work with data and the discussed technologies and methods. If you have any questions or suggestions, feel free to contact us. We hope to continue to update and improve this course. This course was produced by West Virginia View (http://www.wvview.org/) with support from AmericaView (https://americaview.org/). This material is based upon work supported by the U.S. Geological Survey under Grant/Cooperative Agreement No. G18AP00077. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the U.S. Geological Survey. Mention of trade names or commercial products does not constitute their endorsement by the U.S. Geological Survey. After completing this course you will be able to: apply QGIS to visualize, query, and analyze vector and raster spatial data. use available resources to further expand your knowledge of open-source technologies. describe and use a variety of open data formats. code in Python at an intermediate-level. read, summarize, visualize, and analyze data using open Python libraries. create spatial predictive models using Python and associated libraries. use SQL to perform table and spatial queries at an intermediate-level.

Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work.

This data release contains the analytical results and evaluated source data files of geospatial analyses for identifying areas in Alaska that may be prospective for different types of lode gold deposits, including orogenic, reduced-intrusion-related, epithermal, and gold-bearing porphyry. The spatial analysis is based on queries of statewide source datasets of aeromagnetic surveys, Alaska Geochemical Database (AGDB3), Alaska Resource Data File (ARDF), and Alaska Geologic Map (SIM3340) within areas defined by 12-digit HUCs (subwatersheds) from the National Watershed Boundary dataset. The packages of files available for download are: 1. LodeGold_Results_gdb.zip - The analytical results in geodatabase polygon feature classes which contain the scores for each source dataset layer query, the accumulative score, and a designation for high, medium, or low potential and high, medium, or low certainty for a deposit type within the HUC. The data is described by FGDC metadata. An mxd file, and cartographic feature classes are provided for display of the results in ArcMap. An included README file describes the complete contents of the zip file. 2. LodeGold_Results_shape.zip - Copies of the results from the geodatabase are also provided in shapefile and CSV formats. The included README file describes the complete contents of the zip file. 3. LodeGold_SourceData_gdb.zip - The source datasets in geodatabase and geotiff format. Data layers include aeromagnetic surveys, AGDB3, ARDF, lithology from SIM3340, and HUC subwatersheds. The data is described by FGDC metadata. An mxd file and cartographic feature classes are provided for display of the source data in ArcMap. Also included are the python scripts used to perform the analyses. Users may modify the scripts to design their own analyses. The included README files describe the complete contents of the zip file and explain the usage of the scripts. 4. LodeGold_SourceData_shape.zip - Copies of the geodatabase source dataset derivatives from ARDF and lithology from SIM3340 created for this analysis are also provided in shapefile and CSV formats. The included README file describes the complete contents of the zip file.

This dataset holds all materials for the Inform E-learning GIS course

This dataset was created to be used in my Capstone Project for the Google Data Analytics Professional Certificate. Data was web scraped from the state websites to combine the GIS information like FIPS, latitude, longitude, and County Codes by both number and Mailing Number.

RStudio was used for this web scrape and join. For details on how it was done you can go to the following link for my Github repository.

Feel free to follow my Github or LinkedIn profile to see what I end up doing with this Dataset.

This dataset combines the work of several different projects to create a seamless data set for the contiguous United States. Data from four regional Gap Analysis Projects and the LANDFIRE project were combined to make this dataset. In the northwestern United States (Idaho, Oregon, Montana, Washington and Wyoming) data in this map came from the Northwest Gap Analysis Project. In the southwestern United States (Colorado, Arizona, Nevada, New Mexico, and Utah) data used in this map came from the Southwest Gap Analysis Project. The data for Alabama, Florida, Georgia, Kentucky, North Carolina, South Carolina, Mississippi, Tennessee, and Virginia came from the Southeast Gap Analysis Project and the California data was generated by the updated California Gap land cover project. The Hawaii Gap Analysis project provided the data for Hawaii. In areas of the county (central U.S., Northeast, Alaska) that have not yet been covered by a regional Gap Analysis Project, data from the Landfire project was used. Similarities in the methods used by these projects made possible the combining of the data they derived into one seamless coverage. They all used multi-season satellite imagery (Landsat ETM+) from 1999-2001 in conjunction with digital elevation model (DEM) derived datasets (e.g. elevation, landform) to model natural and semi-natural vegetation. Vegetation classes were drawn from NatureServe's Ecological System Classification (Comer et al. 2003) or classes developed by the Hawaii Gap project. Additionally, all of the projects included land use classes that were employed to describe areas where natural vegetation has been altered. In many areas of the country these classes were derived from the National Land Cover Dataset (NLCD). For the majority of classes and, in most areas of the country, a decision tree classifier was used to discriminate ecological system types. In some areas of the country, more manual techniques were used to discriminate small patch systems and systems not distinguishable through topography. The data contains multiple levels of thematic detail. At the most detailed level natural vegetation is represented by NatureServe's Ecological System classification (or in Hawaii the Hawaii GAP classification). These most detailed classifications have been crosswalked to the five highest levels of the National Vegetation Classification (NVC), Class, Subclass, Formation, Division and Macrogroup. This crosswalk allows users to display and analyze the data at different levels of thematic resolution. Developed areas, or areas dominated by introduced species, timber harvest, or water are represented by other classes, collectively refered to as land use classes; these land use classes occur at each of the thematic levels. Raster data in both ArcGIS Grid and ERDAS Imagine format is available for download at http://gis1.usgs.gov/csas/gap/viewer/land_cover/Map.aspx Six layer files are included in the download packages to assist the user in displaying the data at each of the Thematic levels in ArcGIS. In adition to the raster datasets the data is available in Web Mapping Services (WMS) format for each of the six NVC classification levels (Class, Subclass, Formation, Division, Macrogroup, Ecological System) at the following links. http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Class_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Subclass_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Formation_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Division_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Macrogroup_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_Ecological_Systems_Landuse/MapServer

Summary This feature class documents the fire history on CMR from 1964 - present. This is 1 of 2 feature classes, a polygon and a point. This data has a variety of different origins which leads to differing quality of data. Within the polygon feature class, this contains perimeters that were mapped using a GPS, hand digitized, on-screen digitized, and buffered circles to the estimated acreage. These 2 files should be kept together. Within the point feature class, fires with only a location of latitude/longitude, UTM coordinate, TRS and no estimated acreage were mapped using a point location. GPS started being used in 1992 when the technology became available. Records from FMIS (Fire Management Information System) were reviewed and compared to refuge records. Polygon data in FMIS only occurs from 2012 to current and many acreage estimates did not match. This dataset includes ALL fires no matter the size. This feature class documents the fire history on CMR from 1964 - present. This is 1 of 2 feature classes, a polygon and a point. This data has a variety of different origins which leads to differing quality of data. Within the polygon feature class, this contains perimeters that were mapped using a GPS, hand digitized, on-screen digitized, and buffered circles to the estimated acreage. These 2 files should be kept together. Within the point feature class, fires with only a location of latitude/longitude, UTM coordinate, TRS and no estimated acreage were mapped using a point location. GPS started being used in 1992 when the technology became available. Data origins include: Data origins include: 1) GPS Polygon-data (Best), 2) GPS Lat/Long or UTM, 3)TRS QS, 4)TRS Point, 6)Hand digitized from topo map, 7) Circle buffer, 8)Screen digitized, 9) FMIS Lat/Long. Started compiling fire history of CMR in 2007. This has been a 10 year process.FMIS doesn't include fires polygons that are less than 10 acres. This dataset has been sent to FMIS for FMIS records to be updated with correct information. The spreadsheet contains 10-15 records without spatial information and weren't included in either feature class. Fire information from 1964 - 1980 came from records Larry Eichhorn, BLM, provided to CMR staff. Mike Granger, CMR Fire Management Officer, tracked fires on an 11x17 legal pad and all this information was brought into Excel and ArcGIS. Frequently, other information about the fires were missing which made it difficult to back track and fill in missing data. Time was spent verifiying locations that were occasionally recorded incorrectly (DMS vs DD) and converting TRS into Lat/Long and/or UTM. CMR is divided into 2 different UTM zones, zone 12 and zone 13. This occasionally caused errors in projecting. Naming conventions caused confusion. Fires are frequently names by location and there are several "Soda Creek", "Rock Creek", etc fires. Fire numbers were occasionally missing or incorrect. Fires on BLM were included if they were "Assists". Also, fires on satellite refuges and the district were also included. Acreages from GIS were compared to FMIS acres. Please see documentation in ServCat (URL) to see how these were handled.

The National Hydrography Dataset Plus (NHDplus) maps the lakes, ponds, streams, rivers and other surface waters of the United States. Created by the US EPA Office of Water and the US Geological Survey, the NHDPlus provides mean annual and monthly flow estimates for rivers and streams. Additional attributes provide connections between features facilitating complicated analyses. For more information on the NHDPlus dataset see the NHDPlus v2 User Guide.Dataset SummaryPhenomenon Mapped: Surface waters and related features of the United States and associated territories not including Alaska.Geographic Extent: The United States not including Alaska, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaProjection: Web Mercator Auxiliary Sphere Visible Scale: Visible at all scales but layer draws best at scales larger than 1:1,000,000Source: EPA and USGSUpdate Frequency: There is new new data since this 2019 version, so no updates planned in the futurePublication Date: March 13, 2019Prior to publication, the NHDPlus network and non-network flowline feature classes were combined into a single flowline layer. Similarly, the NHDPlus Area and Waterbody feature classes were merged under a single schema.Attribute fields were added to the flowline and waterbody layers to simplify symbology and enhance the layer's pop-ups. Fields added include Pop-up Title, Pop-up Subtitle, On or Off Network (flowlines only), Esri Symbology (waterbodies only), and Feature Code Description. All other attributes are from the original NHDPlus dataset. No data values -9999 and -9998 were converted to Null values for many of the flowline fields.What can you do with this layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS OnlineAdd this layer to a map in the map viewer. The layer is limited to scales of approximately 1:1,000,000 or larger but a vector tile layer created from the same data can be used at smaller scales to produce a webmap that displays across the full range of scales. The layer or a map containing it can be used in an application. Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Apply filters. For example you can set a filter to show larger streams and rivers using the mean annual flow attribute or the stream order attribute. Change the layer’s style and symbologyAdd labels and set their propertiesCustomize the pop-upUse as an input to the ArcGIS Online analysis tools. This layer works well as a reference layer with the trace downstream and watershed tools. The buffer tool can be used to draw protective boundaries around streams and the extract data tool can be used to create copies of portions of the data.ArcGIS ProAdd this layer to a 2d or 3d map. Use as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class. Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of the ArcGIS Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

Today, deep neural networks are widely used in many computer vision problems, also for geographic information systems (GIS) data. This type of data is commonly used for urban analyzes and spatial planning. We used orthophotographic images of two residential districts from Kielce, Poland for research including urban sprawl automatic analysis with Transformer-based neural network application.Orthophotomaps were obtained from Kielce GIS portal. Then, the map was manually masked into building and building surroundings classes. Finally, the ortophotomap and corresponding classification mask were simultaneously divided into small tiles. This approach is common in image data preprocessing for machine learning algorithms learning phase. Data contains two original orthophotomaps from Wietrznia and Pod Telegrafem residential districts with corresponding masks and also their tiled version, ready to provide as a training data for machine learning models.Transformed-based neural network has undergone a training process on the Wietrznia dataset, targeted for semantic segmentation of the tiles into buildings and surroundings classes. After that, inference of the models was used to test model's generalization ability on the Pod Telegrafem dataset. The efficiency of the model was satisfying, so it can be used in automatic semantic building segmentation. Then, the process of dividing the images can be reversed and complete classification mask retrieved. This mask can be used for area of the buildings calculations and urban sprawl monitoring, if the research would be repeated for GIS data from wider time horizon.Since the dataset was collected from Kielce GIS portal, as the part of the Polish Main Office of Geodesy and Cartography data resource, it may be used only for non-profit and non-commertial purposes, in private or scientific applications, under the law "Ustawa z dnia 4 lutego 1994 r. o prawie autorskim i prawach pokrewnych (Dz.U. z 2006 r. nr 90 poz 631 z późn. zm.)". There are no other legal or ethical considerations in reuse potential.Data information is presented below.wietrznia_2019.jpg - orthophotomap of Wietrznia districtmodel's - used for training, as an explanatory imagewietrznia_2019.png - classification mask of Wietrznia district - used for model's training, as a target imagewietrznia_2019_validation.jpg - one image from Wietrznia district - used for model's validation during training phasepod_telegrafem_2019.jpg - orthophotomap of Pod Telegrafem district - used for model's evaluation after training phasewietrznia_2019 - folder with wietrznia_2019.jpg (image) and wietrznia_2019.png (annotation) images, divided into 810 tiles (512 x 512 pixels each), tiles with no information were manually removed, so the training data would contain only informative tilestiles presented - used for the model during training (images and annotations for fitting the model to the data)wietrznia_2019_vaidation - folder with wietrznia_2019_validation.jpg image divided into 16 tiles (256 x 256 pixels each) - tiles were presented to the model during training (images for validation model's efficiency); it was not the part of the training datapod_telegrafem_2019 - folder with pod_telegrafem.jpg image divided into 196 tiles (256 x 265 pixels each) - tiles were presented to the model during inference (images for evaluation model's robustness)Dataset was created as described below.Firstly, the orthophotomaps were collected from Kielce Geoportal (https://gis.kielce.eu). Kielce Geoportal offers a .pst recent map from April 2019. It is an orthophotomap with a resolution of 5 x 5 pixels, constructed from a plane flight at 700 meters over ground height, taken with a camera for vertical photos. Downloading was done by WMS in open-source QGIS software (https://www.qgis.org), as a 1:500 scale map, then converted to a 1200 dpi PNG image.Secondly, the map from Wietrznia residential district was manually labelled, also in QGIS, in the same scope, as the orthophotomap. Annotation based on land cover map information was also obtained from Kielce Geoportal. There are two classes - residential building and surrounding. Second map, from Pod Telegrafem district was not annotated, since it was used in the testing phase and imitates situation, where there is no annotation for the new data presented to the model.Next, the images was converted to an RGB JPG images, and the annotation map was converted to 8-bit GRAY PNG image.Finally, Wietrznia data files were tiled to 512 x 512 pixels tiles, in Python PIL library. Tiles with no information or a relatively small amount of information (only white background or mostly white background) were manually removed. So, from the 29113 x 15938 pixels orthophotomap, only 810 tiles with corresponding annotations were left, ready to train the machine learning model for the semantic segmentation task. Pod Telegrafem orthophotomap was tiled with no manual removing, so from the 7168 x 7168 pixels ortophotomap were created 197 tiles with 256 x 256 pixels resolution. There was also image of one residential building, used for model's validation during training phase, it was not the part of the training data, but was a part of Wietrznia residential area. It was 2048 x 2048 pixel ortophotomap, tiled to 16 tiles 256 x 265 pixels each.

In this course, you will learn to work within the free and open-source R environment with a specific focus on working with and analyzing geospatial data. We will cover a wide variety of data and spatial data analytics topics, and you will learn how to code in R along the way. The Introduction module provides more background info about the course and course set up. This course is designed for someone with some prior GIS knowledge. For example, you should know the basics of working with maps, map projections, and vector and raster data. You should be able to perform common spatial analysis tasks and make map layouts. If you do not have a GIS background, we would recommend checking out the West Virginia View GIScience class. We do not assume that you have any prior experience with R or with coding. So, don't worry if you haven't developed these skill sets yet. That is a major goal in this course. Background material will be provided using code examples, videos, and presentations. We have provided assignments to offer hands-on learning opportunities. Data links for the lecture modules are provided within each module while data for the assignments are linked to the assignment buttons below. Please see the sequencing document for our suggested order in which to work through the material. After completing this course you will be able to: prepare, manipulate, query, and generally work with data in R. perform data summarization, comparisons, and statistical tests. create quality graphs, map layouts, and interactive web maps to visualize data and findings. present your research, methods, results, and code as web pages to foster reproducible research. work with spatial data in R. analyze vector and raster geospatial data to answer a question with a spatial component. make spatial models and predictions using regression and machine learning. code in the R language at an intermediate level.

This dataset represents a unique compiled environmental data set for the circumpolar Arctic ocean region 45N to 90N region. It consists of 170 layers (mostly marine, some terrestrial) in ArcGIS 10 format to be used with a Geographic Information System (GIS) and which are listed below in detail. Most layers are long-term average raster GRIDs for the summer season, often by ocean depth, and represent value-added products easy to use. The sources of the data are manifold such as the World Ocean Atlas 2009 (WOA09), International Bathimetric Chart of the Arctic Ocean (IBCAO), Canadian Earth System Model 2 (CanESM2) data (the newest generation of models available) and data sources such as plankton databases and OBIS. Ocean layers were modeled and predicted into the future and zooplankton species were modeled based on future data: Calanus hyperboreus (AphiaID104467), Metridia longa (AphiaID 104632), M. pacifica (AphiaID 196784) and Thysanoessa raschii (AphiaID 110711). Some layers are derived within ArcGIS. Layers have pixel sizes between 1215.819573 meters and 25257.72929 meters for the best pooled model, and between 224881.2644 and 672240.4095 meters for future climate data. Data was then reprojected into North Pole Stereographic projection in meters (WGS84 as the geographic datum). Also, future layers are included as a selected subset of proposed future climate layers from the Canadian CanESM2 for the next 100 years (scenario runs rcp26 and rcp85). The following layer groups are available: bathymetry (depth, derived slope and aspect); proximity layers (to,glaciers,sea ice, protected areas, wetlands, shelf edge); dissolved oxygen, apparent oxygen, percent oxygen, nitrogen, phosphate, salinity, silicate (all for August and for 9 depth classes); runoff (proximity, annual and August); sea surface temperature; waterbody temperature (12 depth classes); modeled ocean boundary layers (H1, H2, H3 and Wx).This dataset is used for a M.Sc. thesis by the author, and freely available upon request. For questions and details we suggest contacting the authors. Process_Description: Please contact Moritz Schmid for the thesis and detailed explanations. Short version: We model predicted here for the first time ocean layers in the Arctic Ocean based on a unique dataset of physical oceanography. Moreover, we developed presence/random absence models that indicate where the studied zooplankton species are most likely to be present in the Arctic Ocean. Apart from that, we develop the first spatially explicit models known to science that describe the depth in which the studied zooplankton species are most likely to be at, as well as their distribution of life stages. We do not only do this for one present day scenario. We modeled five different scenarios and for future climate data. First, we model predicted ocean layers using the most up to date data from various open access sources, referred here as best-pooled model data. We decided to model this set of stratification layers after discussions and input of expert knowledge by Professor Igor Polyakov from the International Arctic Research Center at the University of Alaska Fairbanks. We predicted those stratification layers because those are the boundaries and layers that the plankton has to cross for diel vertical migration and a change in those would most likely affect the migration. I assigned 4 variables to the stratification layers. H1, H2, H3 and Wx. H1 is the lower boundary of the mixed layer depth. Above this layer a lot of atmospheric disturbance is causing mixing of the water, giving the mixed layer its name. H2, the middle of the halocline is important because in this part of the ocean a strong gradient in salinity and temperature separates water layers. H3, the isotherm is important, because beneath it flows denser and colder Atlantic water. Wx summarizes the overall width of the described water column. Ocean layers were predicted using machine learning algorithms (TreeNet, Salford Systems). Second, ocean layers were included as predictors and used to predict the presence/random absence, most likely depth and life stage layers for the zooplankton species: Calanus hyperboreus, Metridia longa, Metridia pacifica and Thysanoessa raschii, This process was repeated for future predictions based on the CanESM2 data (see in the data section). For zooplankton species the following layers were developed and for the future. C. hyperboreus: Best-pooled model as well as future predictions (rcp26 including ocean layer(also excluding), rcp85 including oocean layers (also excluding) for 2010 and 2100.For parameters: Presence/random absence, most likely depth and life stage layers M. longa: Best-pooled model as well as future predictions (rcp26 including ocean layer(also excluding), rcp85 including oocean layers (also excluding) for 2010 and 2100. For parameters: Presence/rand... Visit https://dataone.org/datasets/f63d0f6c-7d53-46ce-b755-42a368007601 for complete metadata about this dataset.

Active non-wholesale non-transmission Natural Gas Utility Service Areas as listed in the Regulatory Commission of Alaska (RCA) Certificate details for regulated utilities. Likely the most comprehensive collection of State of Alaska utility service areas - but not necessarily definitive for every utility. For complicated large city service areas such as water and sewer the GIS department that represents those cities might have the best representation of the service area. There are also utilities that may not be regulated by RCA which will not be in the data. Footprints in general were lifted from existing KML files created by a contractor in the years 2008-2017. Service area changes that have happened since 2008 may not yet be reflected in the footprints. In a few cases legal descriptions had typos which resulted in service areas miles from the community they intended to cover. In the case of the AsOfDate attribute in this dataset only reflects the date of the last syncing of master certificate metadata with RCA Library database - not the current polygon representation.Source: Regulatory Commission of AlaskaThis data has been visualized in a Geographic Information Systems (GIS) format and is provided as a service in the DCRA Information Portal by the Alaska Department of Commerce, Community, and Economic Development Division of Community and Regional Affairs (SOA DCCED DCRA), Research and Analysis section. SOA DCCED DCRA Research and Analysis is not the authoritative source for this data. For more information and for questions about this data, see: Regulatory Commission of Alaska Library

CDFW BIOS GIS Dataset, Contact: Dana Morawitz, Description: Cal-IPC, with funding from the California Wildlife Conservation Board (CWCB) Prop 1 Stream Flow Enhancement Program, has mapped Arundo donax across the Central Valley at high resolution to serve as a foundation for removal projects. Our goal is for this dataset to help RCDs and others plan successful Arundo control programs and get funding to implement them. The Arundo distribution data ensures that a control program can be implemented strategically over an entire watershed, from top to bottom.

The New Hampshire Hydrography Dataset (NHHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the state's surface water drainage system. The NHHD, developed at 1:24,000 scale, is an extract from the high-resolution National Hydrography Dataset (NHD) housed at the US Geological Survey.The NHHD Shapefile Extract contains the NHDFlowline, NHDWaterbody and NHDArea feature classes from the original NHHD geodatabase. These shapefiles cover the extent of the sixteen cataloging units that intersect the State of NH, and contain reach codes for networked features, stream order, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the the Atlantic Ocean. However, because this data is no longer contained in the original geodatabase, the networking capabilities of the NHDFlowline has been lost. This dataset contains data published by USGS in April 2019.

This dataset attempts to represent the point locations of every educational program in the state of Minnesota that is currently operational and reporting to the Minnesota Department of Education. It can be used to identify schools, various individual school programs, school districts (by office location), colleges, and libraries, among other programs. Please note that not all school programs are statutorily required to report, and many types of programs can be reported at any time of the year, so this dataset is by nature an incomplete snapshot in time.

Maintenance of these locations is a result of an ongoing project to identify current school program locations where Food and Nutrition Services Office (FNS) programs are utilized. The FNS Office is in the Minnesota Department of Education (MDE). GIS staff at MDE maintain the dataset using school program and physical addresses provided by local education authorities (LEAs) for an MDE database called "MDE ORG". MDE GIS staff track weekly changes to program locations, along with comprehensive reviews each summer. All records have been reviewed for accuracy or edited at least once since January 1, 2020.

Note that there may remain errors due to the number of program locations and inconsistency in reporting from LEAs and other organizations. Some organization types (such as colleges and treatment programs) are not subject to annual reporting requirements, so various records included in this file may in fact be inactive or inaccurately located.

Note that multiple programs may occur at the same location and are represented as separate records. For example, an elementary and secondary school may be in the same building, but each has a separate record in the data layer. Users may leverage the "CLASS" and "ORGTYPE" attributes to filter and sort records according to their needs. In general, records at the same physical address will be located at the same coordinates.

This data is also available in CSV format. For that format only, OBJECTID and Shape columns are removed, and the Shape column is replaced by Latitude and Longitude columns.

Abstract

This dataset was derived from provided by Geoscience Australia and the Queensland Department of Natural Resources and Mining. You can find a link to the source dataset in the Lineage Field in this metadata statement. The History Field in this metadata statement describes how this dataset was derived.

This dataset contains a set of feature classes containing GIS point data and attributes derived from tabular GAB_hydrochem_QCedforCSGwaters11mapNO_CONFIDENTIAL.xlsx

• GUID: (8e5cade2-c95a-45ec-9e6e-39b136d2b6d6).

The feature classes are split up according to each bore assigned aquifer extent using ArcGIS GIS processes to produce point, contour and raster data surfaces.

Prior to being converted into GIS Feature Class, the source data had been through a quality assurance and validation process to ensure the reliability of chemistry sample values as well as to check the assigned aquifer formation for each bore.

Purpose

This dataset was created to display groundwater chemistry information spatially for each individual aquifer in the Surat Basin subregion. Information may be displayed as point data, or interpolated to display as contour and raster surfaces.

Dataset History

This dataset was created by converting tabular bore data to GIS feature class point data.

The conversion was done using ArcCatalog.

For each Aquifer in the source dataset - GUID: 8e5cade2-c95a-45ec-9e6e-39b136d2b6d6 (sheets were split up by aquifer formation name) a new point Feature Class was created.

Using ArcGIS GIS processesand tools, these point feature classes were derived: pH, Alkalinity and Total Dissolved Solids (TDS) for each aquifer extent.

These point feature classes were processed using ArcGIS tools to create pH, Alkalinity and TDS (Hydrochemistry) grids.

Contour feature class were derived from the grids.

The grids and contours were clipped to its own aquifer extent.

Dataset Citation

Bioregional Assessment Programme (XXXX) MBC Aquifer Hydrochemistry v01. Bioregional Assessment Derived Dataset. Viewed 11 April 2016, http://data.bioregionalassessments.gov.au/dataset/dc1435a3-ada3-46fc-af17-d2c0d49a929b.

Dataset Ancestors

• Derived From Surface Geology of Australia, 1:1 000 000 scale, 2012 edition

• Derived From GABATLAS - Adori-Springbok Aquifer - Thickness and Extent

• Derived From Gippsland Project Boundary

• Derived From Bioregional Assessment areas v04

• Derived From GABATLAS - Evergreen-Poolowanna Aquitard and Equivalents - Thickness and Extent

• Derived From Natural Resource Management (NRM) Regions 2010

• Derived From GABATLAS - Birkhead-Walloon Aquitard - Thickness and Extent

• Derived From GABATLAS - Precipice Aquifer & Equivalents - Thickness and Extent

• Derived From GABATLAS - Rolling Downs Aquitard - Thickness and Extent

• Derived From Geoscience Australia, National Collaborative Framework Hydrochemical Characterisation Project - Surat Basin Hydrochemistry Spreadsheets

• Derived From Bioregional Assessment areas v03

• Derived From QLD Geological Digital Data - QLD Geology, Structural Framework, November 2012

• Derived From GABATLAS - Cadna-owie-Hooray Aquifer and Equivalents - Thickness and Extent

• Derived From Bioregional Assessment areas v01

• Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

• Derived From GEODATA TOPO 250K Series 3

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From Victoria - Seamless Geology 2014

• Derived From GABATLAS - Hutton Aquifer and Equivalents - Thickness and Extent

• Derived From Geological Provinces - Full Extent

• Derived From Bioregional Assessment areas v02

This is a collection of Canopy Height rasters covering selected U.S. Forest Service and adjoining lands in the Southwest Region, encompassing Arizona and New Mexico. The data are presented in a time-enabled format, allowing the end-user to view available data year-by-year, or all available years at once, within a GIS system. The data encompass varying years, varying resolutions, and varying geographic extents, dependent upon available data as provided by the region. The data represents the height of vegetation above ground, measured in meters.The data contains an attribute table. Notable attributes that may be of interest to an end-user are:lowps: the pixel size of the source raster, given in meters.highps: the pixel size of the top-most pyramid for the raster, given in meters.beginyear: the first year of data acquisition for an individual dataset.endyear: the final year of data acquisition for an individual dataset.dataset_name: the name of the individual dataset within the collection.metadata: A URL link to a file on IIPP's Portal containing metadata pertaining to an individual dataset within the image service.resolution: The pixel size of the source raster, given in meters.Canopy Height data are primarily derived from Lidar datasets. Consequently, these derivatives inherit the limitations and uncertainties of the parent sensor and platform and the processing techniques used to produce the imagery. The images are orthographic; they have been georeferenced and displacement due to sensor orientation and topography have been removed, producing data that combines the characteristics of an image with the geometric qualities of a map. The orthographic images show ground features in their proper positions, without the distortion characteristic of unrectified aerial or satellite imagery. Digital orthoimages produced and used within the Forest Service are developed from imagery acquired through various national and regional image acquisition programs. The resulting orthoimages can be directly applied in remote sensing, GIS and mapping applications. They serve a variety of purposes, from interim maps to references for Earth science investigations and analysis. Because of the orthographic property, an orthoimage can be used like a map for measurement of distances, angles, and areas with scale being constant everywhere. Also, they can be used as map layers in GIS or other computer-based manipulation, overlaying, and analysis. An orthoimage differs from a map in a manner of depiction of detail; on a map only selected detail is shown by conventional symbols whereas on an orthoimage all details appear just as in original aerial or satellite imagery.Tribal lands have been masked from this public service in accordance with Tribal agreements.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

The DNR Hydrography Dataset is the centralized SDE data storage for master versions of DNR hydrographic spatial features (effective 2012). It includes the authoritative versions of the following statewide feature classes:

• DNR Hydrography - DNR Watersheds - Level 08 - All Catchments (polygons) https://gisdata.mn.gov/dataset/geos-dnr-watersheds


• DNR Hydrography - All Water Features (polygons)


• DNR Hydrography - Public Waters Basins (polygons) https://gisdata.mn.gov/dataset/water-mn-public-waters


• DNR Hydrography - Stream Routes with Kittle Numbers and Mile Measures (measured polyline routes) https://gisdata.mn.gov/dataset/water-measured-kittle-routes


• DNR Hydrography - Hydrologic Points of Interest (HPOI) (points) - in development

The DNR Hydrography Dataset is a collection of the "best available" DNR spatial features representing MN surficial hydrology. These features originate from multiple sources representing a range of scales and accuracies. All feature classes are (will eventually be) topologically related and will function as an integrated set of statewide features. Most DNR hydro-related layers are (will eventually be) derived from this central data storage so that master features may be edited once and then remain synchronized among all derived layers.

NOTE: The DNR Hydrography dataset replaces the older DNR 24K and DNR 100K data layers, including those derived layers listed below. Users should discontinue use of these older layers.

100K: dnr_100k_hydro_area_features, dnr_100k_hydro_stream_centerlines, lake_dnrpy2, wetl_dnrpy2, strm_usgsln2
24K: dnr_24k_lakes_and_open_water, dnr_24k_rivers_and_streams, dnr_24k_perennial_stream, lake_openwpy3, strm_baseln3, strm_pwiln3

This Zoning feature class is an element of the Oregon GIS Framework statewide, Zoning spatial data. This version is authorized for public use. Attributes include zoning districts that have been generalized to state classes. As of June 30, 2023, this feature class contains zoning data from 229 local jurisdictions. DLCD plans to continue adding to and updating this statewide zoning dataset as they receive zoning information from the local jurisdictions. Jurisdictions included in the latest version of the statewide zoning geodatabase:

Cities: Adams, Adrian, Albany, Amity, Antelope, Ashland, Astoria, Athena, Aurora, Banks, Barlow, Bay City, Beaverton, Bend, Boardman, Bonanza, Brookings, Brownsville, Burns, Butte Falls, Canby, Cannon Beach, Carlton, Cascade Locks, Cave Junction, Central Point, Chiloquin, Coburg, Columbia City, Coos Bay, Cornelius, Corvallis, Cottage Grove, Creswell, Culver, Dayton, Detroit, Donald, Drain, Dufur, Dundee, Dunes City, Durham, Eagle Point, Echo, Enterprise, Estacada, Eugene, Fairview, Falls City, Florence, Forest Grove, Fossil, Garibaldi, Gaston, Gates, Gearhart, Gervais, Gladstone, Gold Beach, Gold Hill, Grants Pass, Grass Valley, Gresham, Halsey, Happy Valley, Harrisburg, Helix, Hermiston, Hillsboro, Hines, Hood River, Hubbard, Idanha, Independence, Jacksonville, Jefferson, Johnson City, Jordan Valley, Junction City, Keizer, King City, Klamath Falls, La Grande, La Pine, Lafayette, Lake Oswego, Lebanon, Lincoln City, Lowell, Lyons, Madras, Malin, Manzanita, Maupin, Maywood Park, McMinnville, Medford, Merrill, Metolius, Mill City, Millersburg, Milton-Freewater, Milwaukie, Mitchell, Molalla, Monmouth, Moro, Mosier, Mount Angel, Myrtle Creek, Myrtle Point, Nehalem, Newberg, Newport, North Bend, North Plains, Nyssa, Oakridge, Ontario, Oregon City, Pendleton, Philomath, Phoenix, Pilot Rock, Port Orford, Portland, Prescott, Prineville, Rainier, Redmond, Reedsport, Rivergrove, Rockaway Beach, Rogue River, Roseburg, Rufus, Saint Helens, Salem, Sandy, Scappoose, Scio, Scotts Mills, Seaside, Shady Cove, Shaniko, Sheridan, Sherwood, Silverton, Sisters, Sodaville, Spray, Springfield, Stanfield, Stayton, Sublimity, Sutherlin, Sweet Home, Talent, Tangent, The Dalles, Tigard, Tillamook, Toledo, Troutdale, Tualatin, Turner, Ukiah, Umatilla, Vale, Veneta, Vernonia, Warrenton, Wasco, Waterloo, West Linn, Westfir, Weston, Wheeler, Willamina, Wilsonville, Winston, Wood Village, Woodburn, Yamhill.

Counties: Baker County, Benton County, Clackamas County, Clatsop County, Columbia County, Coos County, Crook County, Curry County, Deschutes County, Douglas County, Harney County, Hood River County, Jackson County, Jefferson County, Josephine County, Klamath County, Lane County, Lincoln County, Linn County, Malheur County, Marion County, Multnomah County, Polk County, Sherman County, Tillamook County, Umatilla County, Union County, Wasco County, Washington County, Wheeler County, Yamhill County.

R emaining jurisdictions either chose not to share data to incorporate into the public, statewide dataset or did not respond to DLCD’s request for data. These jurisdictions’ attributes are designated “not shared” in the orZDesc field and “NS” in the orZCode field.

This digital dataset was created as part of a U.S. Geological Survey study, done in cooperation with the Monterey County Water Resource Agency, to conduct a hydrologic resource assessment and develop an integrated numerical hydrologic model of the hydrologic system of Salinas Valley, CA. As part of this larger study, the USGS developed this digital dataset of geologic data and three-dimensional hydrogeologic framework models, referred to here as the Salinas Valley Geological Framework (SVGF), that define the elevation, thickness, extent, and lithology-based texture variations of nine hydrogeologic units in Salinas Valley, CA. The digital dataset includes a geospatial database that contains two main elements as GIS feature datasets: (1) input data to the 3D framework and textural models, within a feature dataset called “ModelInput”; and (2) interpolated elevation, thicknesses, and textural variability of the hydrogeologic units stored as arrays of polygonal cells, within a feature dataset called “ModelGrids”. The model input data in this data release include stratigraphic and lithologic information from water, monitoring, and oil and gas wells, as well as data from selected published cross sections, point data derived from geologic maps and geophysical data, and data sampled from parts of previous framework models. Input surface and subsurface data have been reduced to points that define the elevation of the top of each hydrogeologic units at x,y locations; these point data, stored in a GIS feature class named “ModelInputData”, serve as digital input to the framework models. The location of wells used a sources of subsurface stratigraphic and lithologic information are stored within the GIS feature class “ModelInputData”, but are also provided as separate point feature classes in the geospatial database. Faults that offset hydrogeologic units are provided as a separate line feature class. Borehole data are also released as a set of tables, each of which may be joined or related to well location through a unique well identifier present in each table. Tables are in Excel and ascii comma-separated value (CSV) format and include separate but related tables for well location, stratigraphic information of the depths to top and base of hydrogeologic units intercepted downhole, downhole lithologic information reported at 10-foot intervals, and information on how lithologic descriptors were classed as sediment texture. Two types of geologic frameworks were constructed and released within a GIS feature dataset called “ModelGrids”: a hydrostratigraphic framework where the elevation, thickness, and spatial extent of the nine hydrogeologic units were defined based on interpolation of the input data, and (2) a textural model for each hydrogeologic unit based on interpolation of classed downhole lithologic data. Each framework is stored as an array of polygonal cells: essentially a “flattened”, two-dimensional representation of a digital 3D geologic framework. The elevation and thickness of the hydrogeologic units are contained within a single polygon feature class SVGF_3DHFM, which contains a mesh of polygons that represent model cells that have multiple attributes including XY location, elevation and thickness of each hydrogeologic unit. Textural information for each hydrogeologic unit are stored in a second array of polygonal cells called SVGF_TextureModel. The spatial data are accompanied by non-spatial tables that describe the sources of geologic information, a glossary of terms, a description of model units that describes the nine hydrogeologic units modeled in this study. A data dictionary defines the structure of the dataset, defines all fields in all spatial data attributer tables and all columns in all nonspatial tables, and duplicates the Entity and Attribute information contained in the metadata file. Spatial data are also presented as shapefiles. Downhole data from boreholes are released as a set of tables related by a unique well identifier, tables are in Excel and ascii comma-separated value (CSV) format.